


import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
 import optax
 from flax.training.common_utils import onehot

 from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
 from transformers.models.ta.modeling_flax_ta import shift_tokens_right

@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    @slow
    def 						a_     (			self       ):
         snake_case											=							FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''       )
         snake_case											=							AutoTokenizer.from_pretrained('''google/mt5-small'''       )

         snake_case											=							tokenizer('''Hello there'''     ,   return_tensors='''np'''       ).input_ids
         snake_case											=							tokenizer('''Hi I am'''     ,   return_tensors='''np'''       ).input_ids

         snake_case											=							shift_tokens_right(__snake_case     ,   model.config.pad_token_id     ,   model.config.decoder_start_token_id       )

         snake_case											=							model(__snake_case     ,   decoder_input_ids=__snake_case       ).logits
         snake_case											=							optax.softmax_cross_entropy(__snake_case     ,   onehot(__snake_case     ,   logits.shape[-1]       )       ).mean()

         snake_case											=							-(labels.shape[-1] * loss.item())

         snake_case											=							-84.9127
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1E-4       )








def      UpperCAmelCase__		(UpperCamelCase_ = 10_00							):



     """simple docstring"""






     return sum(2 * a * ((a - 1) // 2) for a in range(3				,n + 1							)							)


if __name__ == "__main__":
 print(solution())




